A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease
Abstract Introduction Cognitive impairment (CI) is a common non-motor symptom of Parkinson's disease (PD). However, the diagnosis and prediction of CI progression in PD remain challenging. We aimed to explore a multi-omics framework based on machine learning integrating comprehensive radiomics,...
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Adis, Springer Healthcare
2025-02-01
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| Series: | Neurology and Therapy |
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| Online Access: | https://doi.org/10.1007/s40120-025-00716-y |
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| author | Yang Luo YaQin Xiang JiaBin Liu YuXuan Hu JiFeng Guo |
| author_facet | Yang Luo YaQin Xiang JiaBin Liu YuXuan Hu JiFeng Guo |
| author_sort | Yang Luo |
| collection | DOAJ |
| description | Abstract Introduction Cognitive impairment (CI) is a common non-motor symptom of Parkinson's disease (PD). However, the diagnosis and prediction of CI progression in PD remain challenging. We aimed to explore a multi-omics framework based on machine learning integrating comprehensive radiomics, cerebrospinal fluid biomarkers, and genetics information to identify CI progression in early PD. Methods Patients were first diagnosed with PD without CI at baseline. According to whether CI progressed within 5 years, patients were divided into two groups: PD without CI and PD with CI. Radiomics signatures were extracted from patients’ T1-weighted MRI. We used machine learning methods to construct radiomics, hybrid, and multi-omics models in the training set and validated the models in the testing set. Result In the two groups, we found 7, 23, and 25 radiomics signatures with significant differences in the parietal, temporal, and frontal lobes, respectively. The radiomics model using the 25 signatures of the frontal lobe had an accuracy of 0.833 and an AUC (area under the curve) of 0.879 to predict CI progression. In addition, the hybrid model fused with the cerebrospinal fluid Aβ level had an accuracy of 0.867 and an AUC of 0.916. In our study, the multi-omics model showed the best predictive performance. The accuracy of the multi-omics model was 0.900, and the average AUC value after five-fold cross-validation was 0.928. Conclusion Radiomics signatures have a recognition effect in the CI progression in early PD. Multi-omics frameworks combining radiomics, cerebrospinal fluid biomarkers, and genetic information may be a potential predictor of CI progression in PD. |
| format | Article |
| id | doaj-art-e1647a9c6bac4554b44c4ae86e480b2a |
| institution | DOAJ |
| issn | 2193-8253 2193-6536 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Adis, Springer Healthcare |
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| series | Neurology and Therapy |
| spelling | doaj-art-e1647a9c6bac4554b44c4ae86e480b2a2025-08-20T02:56:20ZengAdis, Springer HealthcareNeurology and Therapy2193-82532193-65362025-02-0114264365810.1007/s40120-025-00716-yA Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s DiseaseYang Luo0YaQin Xiang1JiaBin Liu2YuXuan Hu3JiFeng Guo4Department of Neurology, XiangYa Hospital, Central South UniversityDepartment of Neurology, XiangYa Hospital, Central South UniversityDepartment of Neurology, XiangYa Hospital, Central South UniversityDepartment of Neurology, XiangYa Hospital, Central South UniversityDepartment of Neurology, XiangYa Hospital, Central South UniversityAbstract Introduction Cognitive impairment (CI) is a common non-motor symptom of Parkinson's disease (PD). However, the diagnosis and prediction of CI progression in PD remain challenging. We aimed to explore a multi-omics framework based on machine learning integrating comprehensive radiomics, cerebrospinal fluid biomarkers, and genetics information to identify CI progression in early PD. Methods Patients were first diagnosed with PD without CI at baseline. According to whether CI progressed within 5 years, patients were divided into two groups: PD without CI and PD with CI. Radiomics signatures were extracted from patients’ T1-weighted MRI. We used machine learning methods to construct radiomics, hybrid, and multi-omics models in the training set and validated the models in the testing set. Result In the two groups, we found 7, 23, and 25 radiomics signatures with significant differences in the parietal, temporal, and frontal lobes, respectively. The radiomics model using the 25 signatures of the frontal lobe had an accuracy of 0.833 and an AUC (area under the curve) of 0.879 to predict CI progression. In addition, the hybrid model fused with the cerebrospinal fluid Aβ level had an accuracy of 0.867 and an AUC of 0.916. In our study, the multi-omics model showed the best predictive performance. The accuracy of the multi-omics model was 0.900, and the average AUC value after five-fold cross-validation was 0.928. Conclusion Radiomics signatures have a recognition effect in the CI progression in early PD. Multi-omics frameworks combining radiomics, cerebrospinal fluid biomarkers, and genetic information may be a potential predictor of CI progression in PD.https://doi.org/10.1007/s40120-025-00716-yParkinson’s diseaseCognitive impairmentMachine learningMagnetic resonance imagingCerebrospinal fluidWhole genome sequencing |
| spellingShingle | Yang Luo YaQin Xiang JiaBin Liu YuXuan Hu JiFeng Guo A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease Neurology and Therapy Parkinson’s disease Cognitive impairment Machine learning Magnetic resonance imaging Cerebrospinal fluid Whole genome sequencing |
| title | A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease |
| title_full | A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease |
| title_fullStr | A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease |
| title_full_unstemmed | A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease |
| title_short | A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson’s Disease |
| title_sort | multi omics framework based on machine learning as a predictor of cognitive impairment progression in early parkinson s disease |
| topic | Parkinson’s disease Cognitive impairment Machine learning Magnetic resonance imaging Cerebrospinal fluid Whole genome sequencing |
| url | https://doi.org/10.1007/s40120-025-00716-y |
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